SOB project

Soledad Ferreras

2024-01-12

Son of a Butcher, Yelp’s customer review Analysis

Introduction

Established in December 2020 and located in Dallas Texas (Lower Greenville), Son of a Butcher is a fast-food establishment specialized in hamburgers, that serves up elevated versions of traditional burger sliders, shakes, and fries. The restaurant uses organic Wagyu beef and they characterized by breading their own chicken sliders and making their own black bean patties in-house for vegetarian lovers. Public opinion plays a pivotal role in shaping the success of a restaurant, with platforms like Yelp, TripAdvisor or Google Maps acting as influential conduits for customer feedback. The impact of online reviews on potential diners cannot be overstated, as individuals often turn to these platforms to gauge the experiences of others before deciding where to dine. The collective sentiments expressed through customer reviews provide invaluable insights into the quality of service, food, and overall dining experience. Positive reviews not only attract new customers but also contribute to the establishment’s positive reputation, fostering trust and loyalty. Conversely, negative reviews can dissuade potential patrons and have a lasting impact on the restaurant’s image. In an era where online presence is integral to business success, understanding and managing public opinion is essential for restaurants to thrive and maintain a competitive edge in the dynamic culinary landscape. The primary objective of the present study, is to glean valuable insights into the overall public sentiment regarding the establishment known as ‘Son of a Butcher’, over time. In pursuit of this goal, data was extracted from the Yelp website dedicated to this restaurant https://www.yelp.com/biz/son-of-a-butcher-dallas-3?sort_by=date_asc]. The extraction process involved utilizing R code, can be found in the following link: . This approach aimed to gather pertinent details such as the restaurant’s customer rating, customer comments, dates of reviews(from December 2020 till present date), if the reviews were considered either “useful”, “cool” or “funny” by other Yelp’s users and other insightful information. Comprehending public opinion holds paramount significance for both the restaurant and its audience. For the restaurant, insights derived from thorough analysis of customer reviews can serve as a compass for refining and enhancing its services. Identifying recurring themes in feedback, whether positive or negative, enables the establishment to pinpoint areas for improvement or capitalize on strengths. This self-awareness can contribute to the refinement of menu offerings, customer service protocols, and overall operational efficiency. On the other hand, for readers—whether potential customers or fellow business owners—the findings offer a valuable lens into the restaurant’s strengths and weaknesses. Prospective diners can make informed decisions based on authentic customer experiences, while entrepreneurs in the food industry can draw inspiration from successful practices or learn from the challenges faced by the analyzed restaurant. In essence, understanding public opinion not only fosters the continual growth of the restaurant but also empowers readers with valuable insights for their own benefit. It is crucial to recognize and address the limitations and challenges inherent in this analysis to maintain transparency and credibility. Firstly, it’s important to note that the data is derived exclusively from Yelp, which may introduce a platform-specific bias. Users on Yelp might not represent the entire spectrum of customer opinions, potentially leading to an incomplete portrayal of the restaurant’s public perception. Furthermore, the nature of online reviews itself can introduce biases, as individuals with extremely positive or negative experiences may be more inclined to share their opinions. Additionally, factors such as the timing of the data collection and potential changes in the restaurant’s management or offerings could influence the analysis outcomes. By openly acknowledging these limitations, we aim to provide a more accurate context for the findings, fostering a nuanced understanding of the public opinion landscape surrounding the restaurant. This transparency underscores our commitment to delivering an analysis that is not only insightful but also cognizant of its inherent constraints.

Structure of the Analysis:

The dataset, named “df_SOB,” encompasses 13 variables pertinent to our analysis. These include “User names” representing customer names, “rating” indicating the grade assigned by customers to the establishment on a scale of 1 to 5 stars, “Date” (month/day/year) which has been systematically divided into three distinct variables—namely “weekday,” “month,” and “year”—to facilitate more granular temporal insights. Similarly, the “location” variable, denoting the city and state, has been dissected into “state” for enhanced geographical analysis. The variable “comment” contains the textual content of customer reviews, offering qualitative insights into their experiences. Additionally, three variables—“Useful,” “cool,” and “funny”—capture quantitative measures representing the interactions of other customers with the reviews, indicating the perceived utility, coolness, or humor in the shared feedback. This comprehensive dataset forms the foundation for our in-depth exploration of the public opinion landscape surrounding the establishment. Provide a brief overview of how you plan to structure your exploratory data analysis. Mention the key sections or themes you will be exploring in the subsequent parts of your report.

Figure 1. Rating mean over the years. Figure 1. Rating mean over the years.

Exploratory Data Analysis

Sentiment Analysis

Figure 7. Sentiment Analysis Histogram. Figure 7. Sentiment Analysis Histogram.

Figure 8. Sentiment Analysis over the years. Figure 8. Sentiment Analysis over the years.

To gain insights into customer sentiments expressed in Yelp comments, a sentiment analysis was conducted using the General Inquirer system developed by social psychologists at Harvard University, which relies on the Harvard IV Dictionary.

In this initial analysis, each comment was assigned a sentiment score (SentimentGI). Negative scores denote negative sentiments, positive scores indicate positive sentiments, and values near zero represent neutral sentiments.

Overall, the sentiment analysis suggests that the predominant sentiment expressed in the comments is positive, followed by a smaller fraction expressing negative sentiment, while the presence of neutral sentiment is negligible (see figure 7). This trend remains consistent across the years, as illustrated in figure 8. Specifically, there was a peak in customer posts in 2021, which gradually decreased in the following years.

Are there specific keywords that frequently appear

in positive or negative reviews?

To explore specific keywords that commonly appear in positive or negative reviews, we analyzed the top 10 words associated with each sentiment category using the “loughran” lexicon. As previously demonstrated, positive sentiment prevails, with frequently occurring words such as ‘good,’ ‘great,’ and ‘friendly.’ This suggests that in addition to food quality, customers emphasize the importance of friendly service, a key aspect that undoubtedly contributes to remarkable ratings

Figure 9. Top 10 words contribution to sentiment.

Users Interactions with Reviews

As described in the introduction, Yelp’s website provides users with the option to engage with customer reviews by selecting one of three reactions: ‘useful,’ ‘cool,’ or ‘funny.’ To explore potential variations in user interaction based on review ratings, we plotted the counts of these interaction variables against the ratings. Figure 10 illustrates that while the majority of comments receive no user feedback, there appears to be a slight positive trend where user interaction increases with higher ratings, primarily through ‘useful’ and ‘cool’ reactions.

When analyzing the relationship between comments’ sentiment scores (SentimentGI) and user interactions (useful, cool, and funny), no discernible pattern emerged. This suggests that Yelp users do not exhibit any specific reaction patterns in response to the language used in reviews (data not shown).

Figure 10. Relationship between User Interactions with Customer Reviews and Ratings.

Geographical Analysis

As previously noted, Son of a Butcher restaurant is situated in Texas, hence it’s unsurprising that the majority of reviews originate from this state (out of the 328 posts, approximately 240 are from Texas). However, California and Florida emerge as the second and third most frequent states, respectively (see figure 11).

To explore potential correlations between geographical location and average ratings, ratings were plotted against different locations. However, no significant pattern was observed (see figure 12).

Figure 11. Customers reviews per State.

Figure 12. Users interactions with customers reviews.

Figure 13. Sentiment Analysis for Low-Rating Months in 2023. Figure 13. Sentiment Analysis for Low-Rating Months in 2023.

Conclusion

In conclusion, the restaurant has consistently received high ratings from Yelp users since its inception, consistently averaging between 4 to 5 stars. Although there has been a slight decline in star ratings over the years, the overall rating remains very good.

Upon closer examination of the data from the past year, January, April, August, and November emerge as months with lower customer ratings. However, during these months, only a small number of customers—20 in total—shared their opinions on Yelp, with only a quarter of them rating the restaurant with 1 or 2 stars. Further analysis of the posted comments for these months revealed a predominantly positive sentiment, as depicted in Figure 13. Notably, only three of these comments expressed disappointment with meal quality, and only one mentioned a service error.

When analyzing weekdays, improvements were observed in several days in 2023 compared to the previous year (Mondays, Tuesdays, Saturdays, and Sundays). However, ratings for Thursdays and Fridays saw a decrease from 4.77 to 3.81 stars (2022 - 2023). These days also coincided with a high frequency of customer posts on Yelp, suggesting a need for corrective actions to reverse this trend.

Furthermore, sentiment analysis revealed an overall positive sentiment towards the language used by customers in their reviews. The most frequently occurring words were “good,” “great,” and “friendly,” indicating that customers appreciated not only the food but also the friendly service. User interactions on Yelp were more frequent for posts with higher ratings (4 and 5 stars), with “useful” and “cool” being the most clicked.

Considering user locations, local users were the most frequent, followed by customers from California and Florida. However, no discernible pattern was observed regarding ratings based on customer location.